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GPU-acceleration of stiffness matrix calculation and efficient initialization of EFG meshless methods

机译:GPU加速的刚度矩阵计算和EFG无网格方法的有效初始化

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Meshless methods have a number of virtues in problems concerning crack growth and propagation, large displacements, strain localization and complex geometries, among other. Despite the fact that they do not rely on a mesh, meshless methods require a preliminary step for the identification of the correlation between nodes and Gauss points before building the stiffness matrix. This is implicitly performed with the mesh generation in FEM but must be explicitly done in EFG methods and can be time-consuming. Furthermore, the resulting matrices are more densely populated and the computational cost for the formulation and solution of the problem is much higher than the conventional FEM. This is mainly attributed to the vast increase in interactions between nodes and integration points due to their extended domains of influence. For these reasons, computing the stiffness matrix in EFG meshless methods is a very computationally demanding task which needs special attention in order to be affordable in real-world applications. In this paper, we address the pre-processing phase, dealing with the problem of defining the necessary correlations between nodes and Gauss points and between interacting nodes, as well as the computation of the stiffness matrix. A novel approach is proposed for the formulation of the stiffness matrix which exhibits several computational merits, one of which is its amenability to parallelization which allows the utilization of graphics processing units (GPUs) to accelerate computations.
机译:无网格方法在涉及裂纹扩展和扩展,大位移,应变局部化和复杂几何形状等问题上具有许多优点。尽管它们不依赖于网格,但无网格方法在构建刚度矩阵之前需要一个初步步骤来识别节点和高斯点之间的相关性。这是在FEM中通过网格生成隐式执行的,但必须在EFG方法中显式完成,这可能很耗时。此外,所得矩阵的人口密度更高,并且该问题的公式化和解决方案的计算成本比常规FEM高得多。这主要归因于节点和集成点之间由于其扩展的影响范围而导致的交互作用的大量增加。由于这些原因,在EFG无网格方法中计算刚度矩阵是一项非常需要计算的任务,需要特别注意才能在实际应用中负担得起。在本文中,我们处理预处理阶段,处理的问题是定义节点与高斯点之间以及相互作用的节点之间的必要相关性,以及刚度矩阵的计算。提出了一种新颖的刚度矩阵表示方法,该方法具有多种计算优点,其中之一是其对并行性的适应性,从而可以利用图形处理单元(GPU)来加快计算速度。

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